import streamlit as st
#from transformers import AutoModelForCausalLM, AutoTokenizer

#model_name = "deepseek-ai/DeepSeek-R1"
#tokenizer = AutoTokenizer.from_pretrained(model_name)
#model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True,quantization_config=None)

#model_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
#tokenizer = AutoTokenizer.from_pretrained(model_id)
#model = AutoModelForCausalLM.from_pretrained(model_id)

# Use a pipeline as a high-level helper
from transformers import pipeline



st.title("DeepSeek Chatbot")

prompt = st.text_input("Enter your message:")

if st.button("Run"):
    messages = [{"role": "user", "content": prompt},]
    pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", trust_remote_code=True)
    response= pipe(messages)
    st.text_area("Long Text Box", response, height=1000)